2026-10-13T11:05:21Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33274230 2026-09-29T03:53:49Z category_35618 category_35549 category_35678 portal_234 item_type_6 month_year_09_2026

Spatio-Spectro-Temporal Characterisation and Correction of Dark Current in a Snapshot Hyperspectral Sensor Vikas Sharma (11832086) Matthew Hayden (9481265) Eija Honkavaara (6566807) Surya Kant (10798998) Computer vision and multimedia computation Information and computing sciences Data management and data science Dark current DSNU Hot pixels Hyperspectral Senop HSC-2 Spatio-spectro-temporal analysis UAV imaging Low-cost uncooled snapshot hyperspectral sensors mounted on UAV platforms offer new opportunities for field-scale remote sensing high-throughput phenotyping, but their reliability is constrained by sensor-intrinsic artefacts, particularly dark current. In this study, we present the first spatio-spectro-temporal characterisation and correction of dark current in the Senop HSC-2 dual-CMOS Fabry-Perot snapshot hyperspectral camera. Six controlled dark experiments (∼6,100 image cubes) revealed that dark current in the sensor is highly structured and reproducible, exhibiting CMOS-specific baseline offsets, monotonic temporal drift, pixel-wise dark signal non-uniformity (DSNU) with wavelength-dependent structure, and persistent hot pixels, and exposure-dependent baseline shifts that do not scale linearly with integration time. These results confirm that conventional single-frame dark subtraction is insufficient for quantitative analysis in uncooled snapshot hyperspectral sensors. Building on this characterisation, a modular correction framework was developed to stabilise the dark signal across spatial, spectral, and temporal domains. Across laboratory datasets, the framework reduced temporal drift by 70-85%, DSNU variance by approximately 37.5%, and suppressed >99.9% of persistent hot pixels, substantially improving radiometric stability. Corrected data exhibited simultaneous spatial uniformity, temporal stability, and spectral integrity, enabling downstream radiometric processing without introducing spectral distortion. Application of Senop HSC-2 to UAV-acquired wheat canopy imagery demonstrated effective transfer to field conditions, improving spectral continuity and robustness of vegetation indices after dark current correction. These results establish a transferable calibration approach for affordable snapshot hyperspectral sensors and demonstrate that rigorous dark current correction is essential for achieving quantitative radiometric performance in UAV-based phenotyping and precision agriculture applications without active thermal control, extending calibration principles traditionally applied in satellite hyperspectral systems to low-cost UAV snapshot sensors.<p></p> 2026-08-01T00:00:00Z Text Journal contribution 10.1016/j.ophoto.2026.100145 https://figshare.com/articles/journal_contribution/Spatio-Spectro-Temporal_Characterisation_and_Correction_of_Dark_Current_in_a_Snapshot_Hyperspectral_Sensor/33274230 CC BY 4.0